Harmonization of glutamic acid decarboxylase and islet antigen-2 autoantibody assays for national institute of diabetes and digestive and kidney diseases consortia

Identification of type 1 diabetes risk phenotypes using an outcome-guided clustering analysis

AIMS/HYPOTHESIS

Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal the heterogeneity of the at-risk population by identifying clinically meaningful clusters are lacking. We aimed to identify and characterise clusters of islet autoantibody-positive individuals who share similar characteristics and type 1 diabetes risk.

METHODS

We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention study data (n=1123). The outcome of the analysis was the time to development of type 1 diabetes, and variables in the model included demographic characteristics, genetics, metabolic factors and islet autoantibodies. An independent dataset (the Diabetes Prevention Trial of Type 1 Diabetes Study) (n=706) was used for validation.

RESULTS

The analysis revealed six clusters with varying type 1 diabetes risks, categorised into three groups based on the hierarchy of clusters. Group A comprised one cluster with high glucose levels (median for glucose mean AUC 9.48 mmol/l; IQR 9.16-10.02) and high risk (2-year diabetes-free survival probability 0.42; 95% CI 0.34, 0.51). Group B comprised one cluster with high IA-2A titres (median 287 DK units/ml; IQR 250-319) and elevated autoantibody titres (2-year diabetes-free survival probability 0.73; 95% CI 0.67, 0.80). Group C comprised four lower-risk clusters with lower autoantibody titres and glucose levels (with 2-year diabetes-free survival probability ranging from 0.84-0.99 in the four clusters). Within group C, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels and age. A decision rule for assigning individuals to clusters was developed. Use of the validation dataset confirmed that the clusters can identify individuals with similar characteristics.

CONCLUSIONS/INTERPRETATION

Demographic, metabolic, immunological and genetic markers may be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.

Longitudinal changes in DNA methylation during the onset of islet autoimmunity differentiate between reversion versus progression of islet autoimmunity

Background

Type 1 diabetes (T1D) is preceded by a heterogenous pre-clinical phase, islet autoimmunity (IA). We aimed to identify pre vs. post-IA seroconversion (SV) changes in DNAm that differed across three IA progression phenotypes, those who lose autoantibodies (reverters), progress to clinical T1D (progressors), or maintain autoantibody levels (maintainers).

Methods

This epigenome-wide association study (EWAS) included longitudinal DNAm measurements in blood (Illumina 450K and EPIC) from participants in Diabetes Autoimmunity Study in the Young (DAISY) who developed IA, one or more islet autoantibodies on at least two consecutive visits. We compared reverters - individuals who sero-reverted, negative for all autoantibodies on at least two consecutive visits and did not develop T1D (n=41); maintainers - continued to test positive for autoantibodies but did not develop T1D (n=60); progressors - developed clinical T1D (n=42). DNAm data were measured before (pre-SV visit) and after IA (post-SV visit). Linear mixed models were used to test for differences in pre- vs post-SV changes in DNAm across the three groups. Linear mixed models were also used to test for group differences in average DNAm. Cell proportions, age, and sex were adjusted for in all models. Median follow-up across all participants was 15.5 yrs. (interquartile range (IQR): 10.8-18.7).

Results

The median age at the pre-SV visit was 2.2 yrs. (IQR: 0.8-5.3) in progressors, compared to 6.0 yrs. (IQR: 1.3-8.4) in reverters, and 5.7 yrs. (IQR: 1.4-9.7) in maintainers. Median time between the visits was similar in reverters 1.4 yrs. (IQR: 1-1.9), maintainers 1.3 yrs. (IQR: 1.0-2.0), and progressors 1.8 yrs. (IQR: 1.0-2.0). Changes in DNAm, pre- vs post-SV, differed across the groups at one site (cg16066195) and 11 regions. Average DNAm (mean of pre- and post-SV) differed across 22 regions.

Conclusion

Differentially changing DNAm regions were located in genomic areas related to beta cell function, immune cell differentiation, and immune cell function.

Childhood screening for type 1 diabetes comparing automated multiplex Antibody Detection by Agglutination-PCR (ADAP) with single plex islet autoantibody radiobinding assays

BACKGROUND

Two or more autoantibodies against either insulin (IAA), glutamic acid decarboxylase (GADA), islet antigen-2 (IA-2A) or zinc transporter 8 (ZnT8A) denote stage 1 (normoglycemia) or stage 2 (dysglycemia) type 1 diabetes prior to stage 3 type 1 diabetes. Automated multiplex Antibody Detection by Agglutination-PCR (ADAP) assays in two laboratories were compared to single plex radiobinding assays (RBA) to define threshold levels for diagnostic specificity and sensitivity.

METHODS

IAA, GADA, IA-2A and ZnT8A were analysed in 1504 (54% females) population based controls (PBC), 456 (55% females) doctor's office controls (DOC) and 535 (41% females) blood donor controls (BDC) as well as in 2300 (48% females) patients newly diagnosed (1-10 years of age) with stage 3 type 1 diabetes. The thresholds for autoantibody positivity were computed in 100 10-fold cross-validations to separate patients from controls either by maximizing the χ2-statistics (chisq) or using the 98th percentile of specificity (Spec98). Mean and 95% CI for threshold, sensitivity and specificity are presented.

FINDINGS

The ADAP ROC curves of the four autoantibodies showed comparable AUC in the two ADAP laboratories and were higher than RBA. Detection of two or more autoantibodies using chisq showed 0.97 (0.95, 0.99) sensitivity and 0.94 (0.91, 0.97) specificity in ADAP compared to 0.90 (0.88, 0.95) sensitivity and 0.97 (0.94, 0.98) specificity in RBA. Using Spec98, ADAP showed 0.92 (0.89, 0.95) sensitivity and 0.99 (0.98, 1.00) specificity compared to 0.89 (0.77, 0.86) sensitivity and 1.00 (0.99, 1.00) specificity in the RBA. The diagnostic sensitivity and specificity were higher in PBC compared to DOC and BDC.

INTERPRETATION

ADAP was comparable in two laboratories, both comparable to or better than RBA, to define threshold levels for two or more autoantibodies to stage type 1 diabetes.

FUNDING

Supported by The Leona M. and Harry B. Helmsley Charitable Trust (grant number 2009-04078), the Swedish Foundation for Strategic Research (Dnr IRC15-0067) and the Swedish Research Council, Strategic Research Area (Dnr 2009-1039). AL was supported by the DiaUnion collaborative study, co-financed by EU Interreg ÖKS, Capital Region of Denmark, Region Skåne and the Novo Nordisk Foundation.

Role and molecular mechanism of Salvia miltiorrhiza associated with chemical compounds in the treatment of diabetes mellitus and its complications: A review

Diabetes mellitus (DM) is one of the most prevalent diseases worldwide, greatly impacting patients' quality of life. This article reviews the progress in Salvia miltiorrhiza, an ancient Chinese plant, for the treatment of DM and its associated complications. Extensive studies have been conducted on the chemical composition and pharmacological effects of S miltiorrhiza, including its anti-inflammatory and antioxidant activities. It has demonstrated potential in preventing and treating diabetes and its consequences by improving peripheral nerve function and increasing retinal thickness in diabetic individuals. Moreover, S miltiorrhiza has shown effectiveness when used in conjunction with angiotensin-converting enzyme inhibitors, angiotensin receptor blockers (ARBs), and statins. The safety and tolerability of S miltiorrhiza have also been thoroughly investigated. Despite the established benefits of managing DM and its complications, further research is needed to determine appropriate usage, dosage, long-term health benefits, and safety.

Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review

BACKGROUND

Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies.

METHODS

We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment.

RESULTS

Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation.

CONCLUSIONS

Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops.

Improved Specificity of Glutamate Decarboxylase 65 Autoantibody Measurement Using Luciferase-Based Immunoprecipitation System Assays

Autoantibodies to glutamate decarboxylase (GADA) are widely used in the prediction and classification of type 1 diabetes. GADA radiobinding assays (RBAs) using N-terminally truncated antigens offer improved specificity, but radioisotopes limit the high-throughput potential for population screening. Luciferase-based immunoprecipitation system (LIPS) assays are sensitive and specific alternatives to RBAs with the potential to improve risk stratification. The performance of assays using the Nanoluc luciferase (Nluc)-conjugated GAD65 constructs, Nluc-GAD65(96-585) and full length Nluc-GAD65(1-585), were evaluated in 434 well-characterized serum samples from patients with recent-onset type 1 diabetes and first-degree relatives. Nonradioactive, high-throughput LIPS assays are quicker and require less serum than RBAs. Of 171 relatives previously tested single autoantibody positive for autoantibodies to full-length GAD65 by RBA but had not progressed to diabetes, fewer retested positive by LIPS using either truncated (n = 72) or full-length (n = 111) antigen. The Nluc-GAD65(96-585) truncation demonstrated the highest specificity in LIPS assays overall, but in contrast to RBA, N-terminus truncations did not result in a significant increase in disease-specificity compared with the full-length antigen. This suggests that binding of nonspecific antibodies is affected by the conformational changes resulting from addition of the Nluc antigen. Nluc-GAD65(96-585) LIPS assays offer low-blood-volume, high-specificity GADA tests for screening and diagnostics.

ARTICLE HIGHLIGHTS

Universal screening for early detection of chronic autoimmune, metabolic and cardiovascular diseases in the general population using capillary blood (UNISCREEN): low-risk interventional, single-centre, pilot study protocol

INTRODUCTION

Chronic autoimmune (type 1 diabetes and coeliac disease) and metabolic/cardiovascular (type 2 diabetes, dyslipidaemia, hypertension) diseases are highly prevalent across all age ranges representing a major public health burden. Universal screening for prediction/early identification of these conditions is a potential tool for reducing their impact on the general population. The aim of this study is to assess whether universal screening using capillary blood sampling is feasible at a population-based level.

METHODS AND ANALYSIS

This is a low-risk interventional, single-centre, pilot study for a population-based screening programme denominated UNISCREEN. Participants are volunteers aged 1-100 who reside in the town of Cantalupo (Milan, Italy) undergoing: (1) interview collecting demographics, anthropometrics and medical history; (2) capillary blood collection for measurement of type 1 diabetes and coeliac disease-specific autoantibodies and immediate measurement of glucose, glycated haemoglobin and lipid panel by point-of-care devices; (3) venous blood sampling to confirm autoantibody-positivity; (4) blood pressure measurement; (5) fulfilment of a feasibility and acceptability questionnaire. The outcomes are the assessment of feasibility and acceptability of capillary blood screening, the prevalence of presymptomatic type 1 diabetes and undiagnosed coeliac disease, distribution of glucose categories, lipid panel and estimate of cardiovascular risk in the study population. With approximately 3000 inhabitants, the screened population is expected to encompass at least half of its size, approaching nearly 1500 individuals.

ETHICS AND DISSEMINATION

This protocol and the informed consent forms have been reviewed and approved by the San Raffaele Hospital Ethics Committee (approval number: 131/INT/2022). Written informed consent is obtained from all study participants or their parents if aged <18. Results will be published in scientific journals and presented at meetings.

CONCLUSIONS

If proven feasible and acceptable, this universal screening model would pave the way for larger-scale programmes, providing an opportunity for the implementation of innovative public health programmes in the general population.

TRIAL REGISTRATION NUMBER

NCT05841719.

A novel, high-performance, low-volume, rapid luciferase immunoprecipitation system (LIPS) assay to detect autoantibodies to zinc transporter 8

BACKGROUND

Zinc transporter 8 autoantibodies (ZnT8A) are thought to appear close to type 1 diabetes (T1D) onset and can identify high-risk multiple (≥2) autoantibody positive individuals. Radiobinding assays (RBA) are widely used for ZnT8A measurement but have limited sustainability. We sought to develop a novel, high-performance, non-radioactive luciferase immunoprecipitation system (LIPS) assay to replace RBA.

METHODS

A custom dual C-terminal ZnT8 (aa268-369; R325/W325) heterodimeric antigen, tagged with a NanoluciferaseTM (Nluc-ZnT8) reporter, and LIPS assay was developed. Assay performance was evaluated by testing sera from new onset T1D (n = 573), healthy schoolchildren (n = 521), and selected first-degree relatives (FDRs) from the Bart's Oxford family study (n = 617; 164 progressed to diabetes).

RESULTS

In new-onset T1D, ZnT8A levels by LIPS strongly correlated with RBA (Spearman's r = 0.89; P < 0.0001), and positivity was highly concordant (94.3%). At a high specificity (95%), LIPS and RBA had comparable assay performance [LIPS pROC-AUC(95) 0.032 (95% CI: 0.029-0.036); RBA pROC-AUC(95) 0.031 (95% CI: 0.028-0.034); P = 0.376]. Overall, FDRs found positive by LIPS or RBA had a comparable 20-year diabetes risk (52.6% and 59.7%, respectively), but LIPS positivity further stratified T1D risk in FDRs positive for at least one other islet autoantibody detected by RBA (P = 0.0346).

CONCLUSION

This novel, high-performance, cheaper, quicker, higher throughput, low blood volume Nluc-ZnT8 LIPS assay is a safe, non-radioactive alternative to RBA with enhanced sensitivity and ability to discriminate T1D progressors. This method offers an advanced approach to current strategies to screen the general population for T1D risk for immunotherapy trials and to reduce rates of diabetic ketoacidosis at diagnosis.

Transdermal Blood Sampling for C-Peptide Is a Minimally Invasive, Reliable Alternative to Venous Sampling in Children and Adults With Type 1 Diabetes

OBJECTIVE

C-peptide and islet autoantibodies are key type 1 diabetes biomarkers, typically requiring venous sampling, which limits their utility. We assessed transdermal capillary blood (TCB) collection as a practical alternative.

RESEARCH DESIGN AND METHODS

Ninety-one individuals (71 with type 1 diabetes, 20 control; individuals with type 1 diabetes: aged median 14.8 years [interquartile range (IQR) 9.1-17.1], diabetes duration 4.0 years [1.5-7.7]; control individuals: 42.2 years [38.0-52.1]) underwent contemporaneous venous and TCB sampling for measurement of plasma C-peptide. Participants with type 1 diabetes also provided venous serum and plasma, and TCB plasma for measurement of autoantibodies to glutamate decarboxylase, islet antigen-2, and zinc transporter 8. The ability of TCB plasma to detect significant endogenous insulin secretion (venous C-peptide ≥200 pmol/L) was compared along with agreement in levels, using Bland-Altman. Venous serum was compared with venous and TCB plasma for detection of autoantibodies, using established thresholds. Acceptability was assessed by age-appropriate questionnaire.

RESULTS

Transdermal sampling took a mean of 2.35 min (SD 1.49). Median sample volume was 50 µL (IQR 40-50) with 3 of 91 (3.3%) failures, and 13 of 88 (14.7%) <35 µL. TCB C-peptide showed good agreement with venous plasma (mean venous ln[C-peptide] - TCB ln[C-peptide] = 0.008, 95% CI [-0.23, 0.29], with 100% [36 of 36] sensitivity/100% [50 of 50] specificity to detect venous C-peptide ≥200 pmol/L). Where venous serum in multiple autoantibody positive TCB plasma agreed in 22 of 32 (sensitivity 69%), comparative specificity was 35 of 36 (97%). TCB was preferred to venous sampling (type 1 diabetes: 63% vs. 7%; 30% undecided).

CONCLUSIONS

Transdermal capillary testing for C-peptide is a sensitive, specific, and acceptable alternative to venous sampling; TCB sampling for islet autoantibodies needs further assessment.

Gastrointestinal Infections Modulate the Risk for Insulin Autoantibodies as the First-Appearing Autoantibody in the TEDDY Study

OBJECTIVE

To investigate gastrointestinal infection episodes (GIEs) in relation to the appearance of islet autoantibodies in The Environmental Determinants of Diabetes in the Young (TEDDY) cohort.

RESEARCH DESIGN AND METHODS

GIEs on risk of autoantibodies against either insulin (IAA) or GAD (GADA) as the first-appearing autoantibody were assessed in a 10-year follow-up of 7,867 children. Stool virome was characterized in a nested case-control study.

RESULTS

GIE reports (odds ratio [OR] 2.17 [95% CI 1.39-3.39]) as well as Norwalk viruses found in stool (OR 5.69 [1.36-23.7]) at <1 year of age were associated with an increased IAA risk at 2-4 years of age. GIEs reported at age 1 to <2 years correlated with a lower risk of IAA up to 10 years of age (OR 0.48 [0.35-0.68]). GIE reports at any other age were associated with an increase in IAA risk (OR 2.04 for IAA when GIE was observed 12-23 months prior [1.41-2.96]). Impacts on GADA risk were limited to GIEs <6 months prior to autoantibody development in children <4 years of age (OR 2.16 [1.54-3.02]).

CONCLUSIONS

Bidirectional associations were observed. GIEs were associated with increased IAA risk when reported before 1 year of age or 12-23 months prior to IAA. Norwalk virus was identified as one possible candidate factor. GIEs reported during the 2nd year of life were associated with a decreased IAA risk.

Type 1 Diabetes Risk Phenotypes Using Cluster Analysis

Background

Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal clinically meaningful clusters in the at-risk population and allow for non-linear relationships between predictors are lacking. We aimed to identify and characterize clusters of islet autoantibody-positive individuals that share similar characteristics and type 1 diabetes risk.

Methods

We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention (PTP) study data (n=1127). The outcome of the analysis was time to type 1 diabetes and variables in the model included demographics, genetics, metabolic factors and islet autoantibodies. An independent dataset (Diabetes Prevention Trial of Type 1 Diabetes, DPT-1 study) (n=704) was used for validation.

Findings

The analysis revealed 8 clusters with varying type 1 diabetes risks, categorized into three groups. Group A had three clusters with high glucose levels and high risk. Group B included four clusters with elevated autoantibody titers. Group C had three lower-risk clusters with lower autoantibody titers and glucose levels. Within the groups, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels, age, and genetic risk. A decision rule for assigning individuals to clusters was developed. The validation dataset confirms that the clusters can identify individuals with similar characteristics.

Interpretation

Demographic, metabolic, immunological, and genetic markers can be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

BACKGROUND

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially.

APPROACH

An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association for Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association.

CONTENT

Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (HbA1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of HbA1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed.

SUMMARY

The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

SARS-CoV-2 Infection and Development of Islet Autoimmunity in Early Childhood

Importance

The incidence of diabetes in childhood has increased during the COVID-19 pandemic. Elucidating whether SARS-CoV-2 infection is associated with islet autoimmunity, which precedes type 1 diabetes onset, is relevant to disease etiology and future childhood diabetes trends.

Objective

To determine whether there is a temporal relationship between SARS-CoV-2 infection and the development of islet autoimmunity in early childhood.

Design, Setting, and Participants

Between February 2018 and March 2021, the Primary Oral Insulin Trial, a European multicenter study, enrolled 1050 infants (517 girls) aged 4 to 7 months with a more than 10% genetically defined risk of type 1 diabetes. Children were followed up through September 2022.

Exposure

SARS-CoV-2 infection identified by SARS-CoV-2 antibody development in follow-up visits conducted at 2- to 6-month intervals until age 2 years from April 2018 through June 2022.

Main Outcomes and Measures

The development of multiple (≥2) islet autoantibodies in follow-up in consecutive samples or single islet antibodies and type 1 diabetes. Antibody incidence rates and risk of developing islet autoantibodies were analyzed.

Results

Consent was obtained for 885 (441 girls) children who were included in follow-up antibody measurements from age 6 months. SARS-CoV-2 antibodies developed in 170 children at a median age of 18 months (range, 6-25 months). Islet autoantibodies developed in 60 children. Six of these children tested positive for islet autoantibodies at the same time as they tested positive for SARS-CoV-2 antibodies and 6 at the visit after having tested positive for SARS-CoV-2 antibodies. The sex-, age-, and country-adjusted hazard ratio for developing islet autoantibodies when the children tested positive for SARS-CoV-2 antibodies was 3.5 (95% CI, 1.6-7.7; P = .002). The incidence rate of islet autoantibodies was 3.5 (95% CI, 2.2-5.1) per 100 person-years in children without SARS-CoV-2 antibodies and 7.8 (95% CI, 5.3-19.0) per 100 person-years in children with SARS-CoV-2 antibodies (P = .02). Islet autoantibody risk in children with SARS-CoV-2 antibodies was associated with younger age (<18 months) of SARS-CoV-2 antibody development (HR, 5.3; 95% CI, 1.5-18.3; P = .009).

Conclusion and relevance

In young children with high genetic risk of type 1 diabetes, SARS-CoV-2 infection was temporally associated with the development of islet autoantibodies.

Children diagnosed with presymptomatic type 1 diabetes through public health screening have milder diabetes at clinical manifestation

AIMS/HYPOTHESIS

We aimed to determine whether disease severity was reduced at onset of clinical (stage 3) type 1 diabetes in children previously diagnosed with presymptomatic type 1 diabetes in a population-based screening programme for islet autoantibodies.

METHODS

Clinical data obtained at diagnosis of stage 3 type 1 diabetes were evaluated in 128 children previously diagnosed with presymptomatic early-stage type 1 diabetes between 2015 and 2022 in the Fr1da study and compared with data from 736 children diagnosed with incident type 1 diabetes between 2009 and 2018 at a similar age in the DiMelli study without prior screening.

RESULTS

At the diagnosis of stage 3 type 1 diabetes, children with a prior early-stage diagnosis had lower median HbA1c (51 mmol/mol vs 91 mmol/mol [6.8% vs 10.5%], p<0.001), lower median fasting glucose (5.3 mmol/l vs 7.2 mmol/l, p<0.05) and higher median fasting C-peptide (0.21 nmol/l vs 0.10 nmol/l, p<0.001) compared with children without previous early-stage diagnosis. Fewer participants with prior early-stage diagnosis had ketonuria (22.2% vs 78.4%, p<0.001) or required insulin treatment (72.3% vs 98.1%, p<0.05) and only 2.5% presented with diabetic ketoacidosis at diagnosis of stage 3 type 1 diabetes. Outcomes in children with a prior early-stage diagnosis were not associated with a family history of type 1 diabetes or diagnosis during the COVID-19 pandemic. A milder clinical presentation was observed in children who participated in education and monitoring after early-stage diagnosis.

CONCLUSIONS/INTERPRETATION

Diagnosis of presymptomatic type 1 diabetes in children followed by education and monitoring improved clinical presentation at the onset of stage 3 type 1 diabetes.

Possible heterogeneity of initial pancreatic islet beta-cell autoimmunity heralding type 1 diabetes

The etiology of type 1 diabetes (T1D) foreshadows the pancreatic islet beta-cell autoimmune pathogenesis that heralds the clinical onset of T1D. Standardized and harmonized tests of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA), islet antigen-2 (IA-2A), and ZnT8 transporter (ZnT8A) allowed children to be followed from birth until the appearance of a first islet autoantibody. In the Environmental Determinants of Diabetes in the Young (TEDDY) study, a multicenter (Finland, Germany, Sweden, and the United States) observational study, children were identified at birth for the T1D high-risk HLA haploid genotypes DQ2/DQ8, DQ2/DQ2, DQ8/DQ8, and DQ4/DQ8. The TEDDY study was preceded by smaller studies in Finland, Germany, Colorado, Washington, and Sweden. The aims were to follow children at increased genetic risk to identify environmental factors that trigger the first-appearing autoantibody (etiology) and progress to T1D (pathogenesis). The larger TEDDY study found that the incidence rate of the first-appearing autoantibody was split into two patterns. IAA first peaked already during the first year of life and tapered off by 3-4 years of age. GADA first appeared by 2-3 years of age to reach a plateau by about 4 years. Prior to the first-appearing autoantibody, genetic variants were either common or unique to either pattern. A split was also observed in whole blood transcriptomics, metabolomics, dietary factors, and exposures such as gestational life events and early infections associated with prolonged shedding of virus. An innate immune reaction prior to the adaptive response cannot be excluded. Clarifying the mechanisms by which autoimmunity is triggered to either insulin or GAD65 is key to uncovering the etiology of autoimmune T1D.

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

BACKGROUND

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially.

APPROACH

An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association of Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association.

CONTENT

Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (Hb A1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of Hb A1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed.

SUMMARY

The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

Sequential data mining of infection patterns as predictors for onset of type 1 diabetes in genetically at-risk individuals

Infections are implicated in the etiology of type 1 diabetes mellitus (T1DM); however, conflicting epidemiologic evidence makes designing effective strategies for presymptomatic screening and disease prevention difficult. Considering the temporality and combination in which infections occur may provide valuable insights into understanding T1DM etiology but is rarely studied due to limited longitudinal datasets and insufficient analytical techniques. The objective of this work was to demonstrate a computational approach to classify the temporality and combination of infections in presymptomatic T1DM. We present a sequential data mining pipeline that leverages routinely collected infectious disease data from a prospective cohort study, the Environmental Determinants of Diabetes in the Young (TEDDY) study, to extract, interpret, and compare infection sequences. We then utilize this pipeline to assess risk for developing presymptomatic biomarkers of islet autoimmunity and clinical onset of T1DM. Overall, we identified 229 significant sequential rules that increased the risk for developing presymptomatic biomarkers of islet autoimmunity or clinical onset of T1DM. Multiple significant sequential rules involving varicella increased the risk for all presymptomatic biomarker-specific outcomes, while a single significant sequential rule involving parasites significantly increased risk for T1DM. Significant sequential rules involving respiratory illnesses were differentially represented among the presymptomatic biomarkers of islet autoimmunity and clinical onset of T1DM. Risk for T1DM was significantly increased by a single episode of sixth disease at 12 months, representing the only single-event sequence that increased disease risk. Together, these findings provide the first insights into the timing and combination of infections in T1DM etiology, which may ultimately lead to personalized disease screening and prevention strategies. The sequential data mining pipeline developed in this work demonstrates how temporal data mining can be used to address clinically meaningful questions. This method can be adapted to other presymptomatic factors and clinical conditions.

β-Cell Function and Insulin Sensitivity in Youth With Early Type 1 Diabetes From a 2-Hour 7-Sample OGTT

CONTEXT

The oral minimal model is a widely accepted noninvasive tool to quantify both β-cell responsiveness and insulin sensitivity (SI) from glucose, C-peptide, and insulin concentrations during a 3-hour 9-point oral glucose tolerance test (OGTT).

OBJECTIVE

Here, we aimed to validate a 2-hour 7-point protocol against the 3-hour OGTT and to test how variation in early sampling frequency impacts estimates of β-cell responsiveness and SI.

METHODS

We conducted a secondary analysis on 15 lean youth with stage 1 type 1 diabetes (T1D; ≥ 2 islet autoantibodies with no dysglycemia) who underwent a 3-hour 9-point OGTT. The oral minimal model was used to quantitate β-cell responsiveness (φtotal) and insulin sensitivity (SI), allowing assessment of β-cell function by the disposition index (DI = φtotal × SI). Seven- and 5-point 2-hour OGTT protocols were tested against the 3-hour 9-point gold standard to determine agreement between estimates of φtotal and its dynamic and static components, SI, and DI across different sampling strategies.

RESULTS

The 2-hour estimates for the disposition index exhibited a strong correlation with 3-hour measures (r = 0.975; P < .001) with similar results for β-cell responsiveness and SI (r = 0.997 and r = 0.982; P < .001, respectively). The agreement of the 3 estimates between the 7-point 2-hour and 9-point 3-hour protocols fell within the 95% CI on the Bland-Altman grid with a median difference of 16.9% (-35.3 to 32.5), 0.2% (-0.6 to 1.3), and 14.9% (-1.4 to 28.3) for DI, φtotal, and SI. Conversely, the 5-point protocol did not provide reliable estimates of φ dynamic and static components.

CONCLUSION

The 2-hour 7-point OGTT is reliable in individuals with stage 1 T1D for assessment of β-cell responsiveness, SI, and DI. Incorporation of these analyses into current 2-hour diabetes staging and monitoring OGTTs offers the potential to more accurately quantify risk of progression in the early stages of T1D.

Phenotypes Associated With Zones Defined by Area Under the Curve Glucose and C-peptide in a Population With Islet Autoantibodies

OBJECTIVE

Metabolic zones were developed to characterize heterogeneity of individuals with islet autoantibodies.

RESEARCH DESIGN AND METHODS

Baseline 2-h oral glucose tolerance test data from 6,620 TrialNet Pathway to Prevention Study (TNPTP) autoantibody-positive participants (relatives of individuals with type 1 diabetes) were used to form 25 zones from five area under the curve glucose (AUCGLU) rows and five area under the curve C-peptide (AUCPEP) columns. Zone phenotypes were developed from demographic, metabolic, autoantibody, HLA, and risk data.

RESULTS

As AUCGLU increased, changes of glucose and C-peptide response curves (from mean glucose and mean C-peptide values at 30, 60, 90, and 120 min) were similar within the five AUCPEP columns. Among the zones, 5-year risk for type 1 diabetes was highly correlated with islet antigen 2 antibody prevalence (r = 0.96, P < 0.001). Disease risk decreased markedly in the highest AUCGLU row as AUCPEP increased (0.88-0.41; P < 0.001 from lowest AUCPEP column to highest AUCPEP column). AUCGLU correlated appreciably less with Index60 (an indicator of insulin secretion) in the highest AUCPEP column (r = 0.33) than in other columns (r ≥ 0.78). AUCGLU was positively related to "fasting glucose × fasting insulin" and to "fasting glucose × fasting C-peptide" (indicators of insulin resistance) before and after adjustments for Index60 (P < 0.001).

CONCLUSIONS

Phenotypes of 25 zones formed from AUCGLU and AUCPEP were used to gain insights into type 1 diabetes heterogeneity. Zones were used to examine GCRC changes with increasing AUCGLU, associations between risk and autoantibody prevalence, the dependence of glucose as a predictor of risk according to C-peptide, and glucose heterogeneity from contributions of insulin secretion and insulin resistance.

Islet Autoantibody Standardization Program: interlaboratory comparison of insulin autoantibody assay performance in 2018 and 2020 workshops

AIMS/HYPOTHESIS

The Islet Autoantibody Standardization Program (IASP) aims to improve the performance of immunoassays measuring autoantibodies in type 1 diabetes and the concordance of results across laboratories. IASP organises international workshops distributing anonymised serum samples to participating laboratories and centralises the collection and analysis of results. In this report, we describe the results of assays measuring IAA submitted to the IASP 2018 and 2020 workshops.

METHODS

The IASP distributed uniquely coded sera from individuals with new-onset type 1 diabetes, multiple islet autoantibody-positive individuals, and diabetes-free blood donors in both 2018 and 2020. Serial dilutions of the anti-insulin mouse monoclonal antibody HUI-018 were also included. Sensitivity, specificity, area under the receiver operating characteristic curve (ROC-AUC), partial ROC-AUC at 95% specificity (pAUC95) and concordance of qualitative/quantitative results were compared across assays.

RESULTS

Results from 45 IAA assays of seven different formats and from 37 IAA assays of six different formats were submitted to the IASP in 2018 and 2020, respectively. The median ROC-AUC was 0.736 (IQR 0.617-0.803) and 0.790 (IQR 0.730-0.836), while the median pAUC95 was 0.016 (IQR 0.004-0.021) and 0.023 (IQR 0.014-0.026) in the 2018 and 2020 workshops, respectively. Assays largely differed in AUC (IASP 2018 range 0.232-0.874; IASP 2020 range 0.379-0.924) and pAUC95 (IASP 2018 and IASP 2020 range 0-0.032).

CONCLUSIONS/INTERPRETATION

Assay formats submitted to this study showed heterogeneous performance. Despite the high variability across laboratories, the in-house radiobinding assay (RBA) remains the gold standard for IAA measurement. However, novel non-radioactive IAA immunoassays showed a good performance and, if further improved, might be considered valid alternatives to RBAs.

Stratifying risk for onset of type 1 diabetes using islet autoantibody trajectory clustering

AIMS/HYPOTHESIS

Islet autoantibodies can be detected prior to the onset of type 1 diabetes and are important tools for aetiologic studies, prevention trials and disease screening. Current risk stratification models rely on the positivity status of islet autoantibodies alone, but additional autoantibody characteristics may be important for understanding disease onset. This work aimed to determine if a data-driven model incorporating characteristics of islet autoantibody development, including timing, type and titre, could stratify risk for type 1 diabetes onset.

METHODS

Data on autoantibodies against GAD (GADA), tyrosine phosphatase islet antigen-2 (IA-2A) and insulin (IAA) were obtained for 1,415 children enrolled in The Environmental Determinants of Diabetes in the Young study with at least one positive autoantibody measurement from years 1 to 12 of life. Unsupervised machine learning algorithms were trained to identify clusters of autoantibody development based on islet autoantibody timing, type and titre. Risk for type 1 diabetes across each identified cluster was evaluated using time-to-event analysis.

RESULTS

We identified 2-4 clusters in each year cohort that differed by autoantibody timing, titre and type. During the first 3 years of life, risk for type 1 diabetes onset was driven by membership in clusters with high titres of all three autoantibodies (1-year risk: 20.87-56.25%, 5-year risk: 67.73-69.19%). Type 1 diabetes risk transitioned to type-specific titres during ages 4 to 8, as clusters with high titres of IA-2A (1-year risk: 20.88-28.93%, 5-year risk: 62.73-78.78%) showed faster progression to diabetes compared with high titres of GADA (1-year risk: 4.38-6.11%, 5-year risk: 25.06-31.44%). The importance of high GADA titres decreased during ages 9 to 12, with clusters containing high titres of IA-2A alone (1-year risk: 14.82-30.93%) or both GADA and IA-2A (1-year risk: 8.27-25.00%) demonstrating increased risk.

CONCLUSIONS/INTERPRETATION

This unsupervised machine learning approach provides a novel tool for stratifying risk for type 1 diabetes onset using multiple autoantibody characteristics. These findings suggest that age-dependent changes in IA-2A titres modulate risk for type 1 diabetes onset across 12 years of life. Overall, this work supports incorporation of islet autoantibody timing, type and titre in risk stratification models for aetiologic studies, prevention trials and disease screening.

The relationship between islet autoantibody status and the genetic risk of type 1 diabetes in adult-onset type 1 diabetes

AIMS/HYPOTHESIS

The reason for the observed lower rate of islet autoantibody positivity in clinician-diagnosed adult-onset vs childhood-onset type 1 diabetes is not known. We aimed to explore this by assessing the genetic risk of type 1 diabetes in autoantibody-negative and -positive children and adults.

METHODS

We analysed GAD autoantibodies, insulinoma-2 antigen autoantibodies and zinc transporter-8 autoantibodies (ZnT8A) and measured type 1 diabetes genetic risk by genotyping 30 type 1 diabetes-associated variants at diagnosis in 1814 individuals with clinician-diagnosed type 1 diabetes (1112 adult-onset, 702 childhood-onset). We compared the overall type 1 diabetes genetic risk score (T1DGRS) and non-HLA and HLA (DR3-DQ2, DR4-DQ8 and DR15-DQ6) components with autoantibody status in those with adult-onset and childhood-onset diabetes. We also measured the T1DGRS in 1924 individuals with type 2 diabetes from the Wellcome Trust Case Control Consortium to represent non-autoimmune diabetes control participants.

RESULTS

The T1DGRS was similar in autoantibody-negative and autoantibody-positive clinician-diagnosed childhood-onset type 1 diabetes (mean [SD] 0.274 [0.034] vs 0.277 [0.026], p=0.4). In contrast, the T1DGRS in autoantibody-negative adult-onset type 1 diabetes was lower than that in autoantibody-positive adult-onset type 1 diabetes (mean [SD] 0.243 [0.036] vs 0.271 [0.026], p<0.0001) but higher than that in type 2 diabetes (mean [SD] 0.229 [0.034], p<0.0001). Autoantibody-negative adults were more likely to have the more protective HLA DR15-DQ6 genotype (15% vs 3%, p<0.0001), were less likely to have the high-risk HLA DR3-DQ2/DR4-DQ8 genotype (6% vs 19%, p<0.0001) and had a lower non-HLA T1DGRS (p<0.0001) than autoantibody-positive adults. In contrast to children, autoantibody-negative adults were more likely to be male (75% vs 59%), had a higher BMI (27 vs 24 kg/m²) and were less likely to have other autoimmune conditions (2% vs 10%) than autoantibody-positive adults (all p<0.0001). In both adults and children, type 1 diabetes genetic risk was unaffected by the number of autoantibodies (p>0.3). These findings, along with the identification of seven misclassified adults with monogenic diabetes among autoantibody-negative adults and the results of a sensitivity analysis with and without measurement of ZnT8A, suggest that the intermediate type 1 diabetes genetic risk in autoantibody-negative adults is more likely to be explained by the inclusion of misclassified non-autoimmune diabetes (estimated to represent 67% of all antibody-negative adults, 95% CI 61%, 73%) than by the presence of unmeasured autoantibodies or by a discrete form of diabetes. When these estimated individuals with non-autoimmune diabetes were adjusted for, the prevalence of autoantibody positivity in adult-onset type 1 diabetes was similar to that in children (93% vs 91%, p=0.4).

CONCLUSIONS/INTERPRETATION

The inclusion of non-autoimmune diabetes is the most likely explanation for the observed lower rate of autoantibody positivity in clinician-diagnosed adult-onset type 1 diabetes. Our data support the utility of islet autoantibody measurement in clinician-suspected adult-onset type 1 diabetes in routine clinical practice.

Effective assay technologies fit for large-scale population screening of type 1 diabetes

While worldwide prevention efforts for type 1 diabetes (T1D) are underway to abrogate or slow progression to diabetes, mass screening of islet autoantibodies (IAbs) in the general population is urgently needed. IAbs, the most reliable biomarkers, play an essential role in prediction and clinical diagnosis of T1D. Through laboratory proficiency programs and harmonization efforts, a radio-binding assay (RBA) has been well established as the current 'gold' standard assay for all four IAbs. However, in view of the need for large-scale screening in the non-diabetic population, RBA consistently faces two fundamental challenges, cost-efficiency and disease specificity. While all four IAbs are important for disease prediction, the RBA platform, with a separate IAb test format is laborious, inefficient and expensive. Furthermore, the majority of IAb positivity in screening, especially from individuals with single IAb were found to be low risk with low affinity. It is well documented from multiple clinical studies that IAbs with low affinity are low risk with less or no disease relevance. At present, two non-radioactive multiplex assays, a 3-assay ELISA combining three IAbs and a multiplex ECL assay combining all four IAbs, have been successfully used as the primary methods for general population screenings in Germany and the US, respectively. Recently, the TrialNet Pathway to Prevention study has been organizing an IAb workshop which aims to analyze the 5-year T1D predictive values of IAbs. A T1D-specific assay with high efficiency, low cost and requiring low volume of sample will definitely be necessary to benefit general population screening.

Barriers to Screening: An Analysis of Factors Impacting Screening for Type 1 Diabetes Prevention Trials

Context

Participants with stage 1 or 2 type 1 diabetes (T1D) qualify for prevention trials, but factors involved in screening for such trials are largely unknown.

Objective

To identify factors associated with screening for T1D prevention trials.

Methods

This study included TrialNet Pathway to Prevention participants who were eligible for a prevention trial: oral insulin (TN-07, TN-20), teplizumab (TN-10), abatacept (TN-18), and oral hydroxychloroquine (TN-22). Univariate and multivariate logistic regression models were used to examine participant, site, and study factors at the time of prevention trial accrual.

Results

Screening rates for trials were: 50% for TN-07 (584 screened/1172 eligible), 9% for TN-10 (106/1249), 24% for TN-18 (313/1285), 17% for TN-20 (113/667), and 28% for TN-22 (371/1336). Younger age and male sex were associated with higher screening rates for prevention trials overall and for oral therapies. Participants with an offspring with T1D showed lower rates of screening for all trials and oral drug trials compared with participants with other first-degree relatives as probands. Site factors, including larger monitoring volume and US site vs international site, were associated with higher prevention trial screening rates.

Conclusions

Clear differences exist between participants who screen for prevention trials and those who do not screen and between the research sites involved in prevention trial screening. Participant age, sex, and relationship to proband are significantly associated with prevention trial screening in addition to key site factors. Identifying these factors can facilitate strategic recruitment planning to support rapid and successful enrollment into prevention trials.

Progression likelihood score identifies substages of presymptomatic type 1 diabetes in childhood public health screening

AIMS/HYPOTHESIS

The aim of this study was to develop strategies that identify children from the general population who have late-stage presymptomatic type 1 diabetes and may, therefore, benefit from immune intervention.

METHODS

We tested children from Bavaria, Germany, aged 1.75-10 years, enrolled in the Fr1da public health screening programme for islet autoantibodies (n=154,462). OGTT and HbA_1c were assessed in children with multiple islet autoantibodies for diagnosis of presymptomatic stage 1 (normoglycaemia) or stage 2 (dysglycaemia) type 1 diabetes. Cox proportional hazards and penalised logistic regression of autoantibody, genetic, metabolic and demographic information were used to develop a progression likelihood score to identify children with stage 1 type 1 diabetes who progressed to stage 3 (clinical) type 1 diabetes within 2 years.

RESULTS

Of 447 children with multiple islet autoantibodies, 364 (81.4%) were staged. Undiagnosed stage 3 type 1 diabetes, presymptomatic stage 2, and stage 1 type 1 diabetes were detected in 41 (0.027% of screened children), 30 (0.019%) and 293 (0.19%) children, respectively. The 2 year risk for progression to stage 3 type 1 diabetes was 48% (95% CI 34, 58) in children with stage 2 type 1 diabetes (annualised risk, 28%). HbA_1c, islet antigen-2 autoantibody positivity and titre, and the 90 min OGTT value were predictors of progression in children with stage 1 type 1 diabetes. The derived progression likelihood score identified substages corresponding to ≤90th centile (stage 1a, n=258) and >90th centile (stage 1b, n=29; 0.019%) of stage 1 children with a 4.1% (95% CI 1.4, 6.7) and 46% (95% CI 21, 63) 2 year risk of progressing to stage 3 type 1 diabetes, respectively.

CONCLUSIONS/INTERPRETATION

Public health screening for islet autoantibodies found 0.027% of children to have undiagnosed clinical type 1 diabetes and 0.038% to have undiagnosed presymptomatic stage 2 or stage 1b type 1 diabetes, with 50% risk to develop clinical type 1 diabetes within 2 years.

HbA1c as a time predictive biomarker for an additional islet autoantibody and type 1 diabetes in seroconverted TEDDY children

OBJECTIVE

Increased level of glycated hemoglobin (HbA1c) is associated with type 1 diabetes onset that in turn is preceded by one to several autoantibodies against the pancreatic islet beta cell autoantigens; insulin (IA), glutamic acid decarboxylase (GAD), islet antigen-2 (IA-2) and zinc transporter 8 (ZnT8). The risk for type 1 diabetes diagnosis increases by autoantibody number. Biomarkers predicting the development of a second or a subsequent autoantibody and type 1 diabetes are needed to predict disease stages and improve secondary prevention trials. This study aimed to investigate whether HbA1c possibly predicts the progression from first to a subsequent autoantibody or type 1 diabetes in healthy children participating in the Environmental Determinants of Diabetes in the Young (TEDDY) study.

RESEARCH DESIGN AND METHODS

A joint model was designed to assess the association of longitudinal HbA1c levels with the development of first (insulin or GAD autoantibodies) to a second, second to third, third to fourth autoantibody or type 1 diabetes in healthy children prospectively followed from birth until 15 years of age.

RESULTS

It was found that increased levels of HbA1c were associated with a higher risk of type 1 diabetes (HR 1.82, 95% CI [1.57-2.10], p < 0.001) regardless of first appearing autoantibody, autoantibody number or type. A decrease in HbA1c levels was associated with the development of IA-2A as a second autoantibody following GADA (HR 0.85, 95% CI [0.75, 0.97], p = 0.017) and a fourth autoantibody following GADA, IAA and ZnT8A (HR 0.90, 95% CI [0.82, 0.99], p = 0.036). HbA1c trajectory analyses showed a significant increase of HbA1c over time (p < 0.001) and that the increase is more rapid as the number of autoantibodies increased from one to three (p < 0.001).

CONCLUSION

In conclusion, increased HbA1c is a reliable time predictive marker for type 1 diabetes onset. The increased rate of increase of HbA1c from first to third autoantibody and the decrease in HbA1c predicting the development of IA-2A are novel findings proving the link between HbA1c and the appearance of autoantibodies.

Islet Autoantibody Level Distribution in Type 1 Diabetes and Their Association With Genetic and Clinical Characteristics

CONTEXT

The importance of the autoantibody level at diagnosis of type 1 diabetes (T1D) is not clear.

OBJECTIVE

We aimed to assess the association of glutamate decarboxylase (GADA), islet antigen-2 (IA-2A), and zinc transporter 8 (ZnT8A) autoantibody levels with clinical and genetic characteristics at diagnosis of T1D.

METHODS

We conducted a prospective, cross-sectional study. GADA, IA-2A, and ZnT8A were measured in 1644 individuals with T1D at diagnosis using radiobinding assays. Associations between autoantibody levels and the clinical and genetic characteristics for individuals were assessed in those positive for these autoantibodies. We performed replication in an independent cohort of 449 people with T1D.

RESULTS

GADA and IA-2A levels exhibited a bimodal distribution at diagnosis. High GADA level was associated with older age at diagnosis (median 27 years vs 19 years, P = 9 × 10-17), female sex (52% vs 37%, P = 1 × 10-8), other autoimmune diseases (13% vs 6%, P = 3 × 10-6), and HLA-DR3-DQ2 (58% vs 51%, P = .006). High IA-2A level was associated with younger age of diagnosis (median 17 years vs 23 years, P = 3 × 10-7), HLA-DR4-DQ8 (66% vs 50%, P = 1 × 10-6), and ZnT8A positivity (77% vs 52%, P = 1 × 10-15). We replicated our findings in an independent cohort of 449 people with T1D where autoantibodies were measured using enzyme-linked immunosorbent assays.

CONCLUSION

Islet autoantibody levels provide additional information over positivity in T1D at diagnosis. Bimodality of GADA and IA-2A autoantibody levels highlights the novel aspect of heterogeneity of T1D. This may have implications for T1D prediction, treatment, and pathogenesis.

IL-6-targeted therapies to block the cytokine or its receptor drive distinct alterations in T cell function

Therapeutics that inhibit IL-6 at different points in its signaling pathway are in clinical use, yet whether the immunological effects of these interventions differ based on their molecular target is unknown. We performed short-term interventions in individuals with type 1 diabetes using anti-IL-6 (siltuximab) or anti-IL-6 receptor (IL-6R; tocilizumab) therapies and investigated the impact of this in vivo blockade on T cell fate and function. Immune outcomes were influenced by the target of the therapeutic intervention (IL-6 versus IL-6R) and by peak drug concentration. Tocilizumab reduced ICOS expression on T follicular helper cell populations and T cell receptor-driven (TCR-driven) STAT3 phosphorylation. Siltuximab reversed resistance to Treg-mediated suppression and increased TCR-driven phosphorylated STAT3 and production of IL-10, IL-21, and IL-27 by T effectors. Together, these findings indicate that the context of IL-6 blockade in vivo drives distinct T cell-intrinsic changes that may influence therapeutic outcomes.

A longitudinal assessment of diabetes autoantibodies in the SEARCH for diabetes in youth study

To assess changes in diabetes autoantibodies (DAs) over time in children and young adults with diabetes and determine whether observed changes were associated with demographic characteristics, clinical parameters and diabetes complications. Participants had DAs measured at baseline (10.3 ± 7.1 months after diabetes diagnosis) and at 12, 24 months and ≥5 years after the baseline measurement. At the ≥5-year follow-up, the presence of diabetes complications was assessed. We examined the associations between change in number of positive DAs and changes in individual DA status with the participants' characteristics and clinical parameters over time. Out of 4179 participants, 62% had longitudinal DA data and 51% had complications and longitudinal DA data. In participants with ≥1 baseline positive DA (n = 1699), 83.4% remained positive after 7.3 ± 2.3 years duration of diabetes. Decrease in number of positive DAs was associated with longer diabetes duration (p = 0.003 for 1 baseline positive DA; p < 0.001 for 2 baseline positive DAs) and younger age at diagnosis (p < 0.001 for 2 baseline positive DAs). No associations were found between change in number of positive DAs in participants with ≥1 baseline positive DA (n = 1391) and HbA1c, insulin dose, acute, or chronic complications after 7.7 ± 1.9 years duration of diabetes. DA status likely remains stable in the first 7 years after diabetes diagnosis. Younger age at diabetes diagnosis and longer duration were associated with less persistence of DAs. Measuring DAs after initial presentation may aid in diabetes classification but not likely in predicting the clinical course.

Elevations in blood glucose before and after the appearance of islet autoantibodies in children

The etiology of type 1 diabetes has polygenic and environmental determinants that lead to autoimmune responses against pancreatic β cells and promote β cell death. The autoimmunity is considered silent without metabolic consequences until late preclinical stages,and it remains unknown how early in the disease process the pancreatic β cell is compromised. To address this, we investigated preprandial nonfasting and postprandial blood glucose concentrations and islet autoantibody development in 1,050 children with high genetic risk of type 1 diabetes. Pre- and postprandial blood glucose decreased between 4 and 18 months of age and gradually increased until the final measurements at 3.6 years of age. Determinants of blood glucose trajectories in the first year of life included sex, body mass index, glucose-related genetic risk scores, and the type 1 diabetes-susceptible INS gene. Children who developed islet autoantibodies had early elevations in blood glucose concentrations. A sharp and sustained rise in postprandial blood glucose was observed at around 2 months prior to autoantibody seroconversion, with further increases in postprandial and, subsequently, preprandial values after seroconversion. These findings show heterogeneity in blood glucose control in infancy and early childhood and suggest that islet autoimmunity is concurrent or subsequent to insults on the pancreatic islets.

Predictors of the Initiation of Islet Autoimmunity and Progression to Multiple Autoantibodies and Clinical Diabetes: The TEDDY Study

OBJECTIVE

To distinguish among predictors of seroconversion, progression to multiple autoantibodies and from multiple autoantibodies to type 1 diabetes in young children.

RESEARCH DESIGN AND METHODS

Genetically high-risk newborns (n = 8,502) were followed for a median of 11.2 years (interquartile range 9.3-12.6); 835 (9.8%) developed islet autoantibodies and 283 (3.3%) were diagnosed with type 1 diabetes. Predictors were examined using Cox proportional hazards models.

RESULTS

Predictors of seroconversion and progression differed, depending on the type of first appearing autoantibody. Male sex, Finnish residence, having a sibling with type 1 diabetes, the HLA DR4 allele, probiotic use before age 28 days, and single nucleotide polymorphism (SNP) rs689_A (INS) predicted seroconversion to IAA-first (having islet autoantibody to insulin as the first appearing autoantibody). Increased weight at 12 months and SNPs rs12708716_G (CLEC16A) and rs2292239_T (ERBB3) predicted GADA-first (autoantibody to GAD as the first appearing). For those having a father with type 1 diabetes, the SNPs rs2476601_A (PTPN22) and rs3184504_T (SH2B3) predicted both. Younger age at seroconversion predicted progression from single to multiple autoantibodies as well as progression to diabetes, except for those presenting with GADA-first. Family history of type 1 diabetes and the HLA DR4 allele predicted progression to multiple autoantibodies but not diabetes. Sex did not predict progression to multiple autoantibodies, but males progressed more slowly than females from multiple autoantibodies to diabetes. SKAP2 and MIR3681HG SNPs are newly reported to be significantly associated with progression from multiple autoantibodies to type 1 diabetes.

CONCLUSIONS

Predictors of IAA-first versus GADA-first autoimmunity differ from each other and from the predictors of progression to diabetes.

The longitudinal loss of islet autoantibody responses from diagnosis of type 1 diabetes occurs progressively over follow-up and is determined by low autoantibody titres, early-onset, and genetic variants

The clinical usefulness of post-diagnosis islet autoantibody levels is unclear and factors that drive autoantibody persistence are poorly defined in type 1 diabetes (T1D). Our aim was to characterise the longitudinal loss of islet autoantibody responses after diagnosis in a large, prospectively sampled UK cohort. Participants with T1D [n = 577] providing a diagnosis sample [range -1.0 to 2.0 years] and at least one post-diagnosis sample (<32.0 years) were tested for autoantibodies to glutamate decarboxylase 65 (GADA), islet antigen-2 (IA-2A), and zinc transporter 8 (ZnT8A). Select HLA and non-HLA SNPs were considered. Non-genetic and genetic factors were assessed by multivariable logistic regression models for autoantibody positivity at initial sampling and autoantibody loss at final sampling. For GADA, IA-2A, and ZnT8A, 70.8%, 76.8%, and 40.1%, respectively, remained positive at the final sampling. Non-genetic predictors of autoantibody loss were low baseline autoantibody titres (P < 0.0001), longer diabetes duration (P < 0.0001), and age-at-onset under 8 years (P < 0.01--0.05). Adjusting for non-genetic covariates, GADA loss was associated with low-risk HLA class II genotypes (P = 0.005), and SNPs associated with autoimmunity RELA/11q13 (P = 0.017), LPP/3q28 (P = 0.004), and negatively with IFIH1/2q24 (P = 0.018). IA-2A loss was not associated with genetic factors independent of other covariates, while ZnT8A loss was associated with the presence of HLA A*24 (P = 0.019) and weakly negatively with RELA/11q13 (P = 0.049). The largest longitudinal study of islet autoantibody responses from diagnosis of T1D shows that autoantibody loss is heterogeneous and influenced by low titres at onset, longer duration, earlier age-at-onset, and genetic variants. These data may inform clinical trials where post-diagnosis participants are recruited.

Index60 Is Superior to HbA1c for Identifying Individuals at High Risk for Type 1 Diabetes

CONTEXT

HbA1c from ≥ 5.7% to < 6.5% (39-46 mmol/mol) indicates prediabetes according to American Diabetes Association guidelines, yet its identification of prediabetes specific for type 1 diabetes has not been assessed. A composite glucose and C-peptide measure, Index60, identifies individuals at high risk for type 1 diabetes.

OBJECTIVE

We compared Index60 and HbA1c thresholds as markers for type 1 diabetes risk.

METHODS

TrialNet Pathway to Prevention study participants with ≥ 2 autoantibodies (GADA, IAA, IA-2A, or ZnT8A) who had oral glucose tolerance tests and HbA1c measurements underwent 1) predictive time-dependent modeling of type 1 diabetes risk (n = 2776); and 2) baseline comparisons between high-risk mutually exclusive groups: Index60 ≥ 2.04 (n = 268) vs HbA1c ≥ 5.7% (n = 268). The Index60 ≥ 2.04 threshold was commensurate in ordinal ranking with the standard prediabetes threshold of HbA1c ≥ 5.7%.

RESULTS

In mutually exclusive groups, individuals exceeding Index60 ≥ 2.04 had a higher cumulative incidence of type 1 diabetes than those exceeding HbA1c ≥ 5.7% (P < 0.0001). Appreciably more individuals with Index60 ≥ 2.04 were at stage 2, and among those at stage 2, the cumulative incidence was higher for those with Index60 ≥ 2.04 (P = 0.02). Those with Index60 ≥ 2.04 were younger, with lower BMI, greater autoantibody number, and lower C-peptide than those with HbA1c ≥ 5.7% (P < 0.0001 for all comparisons).

CONCLUSION

Individuals with Index60 ≥ 2.04 are at greater risk for type 1 diabetes with features more characteristic of the disorder than those with HbA1c ≥ 5.7%. Index60 ≥ 2.04 is superior to the standard HbA1c ≥ 5.7% threshold for identifying prediabetes in autoantibody-positive individuals. These findings appear to justify using Index60 ≥ 2.04 as a prediabetes criterion in this population.

A classification and regression tree analysis identifies subgroups of childhood type 1 diabetes

Background

Diabetes in childhood and adolescence includes autoimmune and non-autoimmune forms with heterogeneity in clinical and biochemical presentations. An unresolved question is whether there are subtypes, endotypes, or theratypes within these forms of diabetes.

Methods

The multivariable classification and regression tree (CART) analysis method was used to identify subgroups of diabetes with differing residual C-peptide levels in patients with newly diagnosed diabetes before 20 years of age (n=1192). The robustness of the model was assessed in a confirmation and prognosis cohort (n=2722).

Findings

The analysis selected age, haemoglobin A1c (HbA1c), and body mass index (BMI) as split parameters that classified patients into seven islet autoantibody-positive and three autoantibody-negative groups. There were substantial differences in genetics, inflammatory markers, diabetes family history, lipids, 25-OH-Vitamin D3, insulin treatment, insulin sensitivity and insulin autoimmunity among the groups, and the method stratified patients with potentially different pathogeneses and prognoses. Interferon-ɣ and/or tumour necrosis factor inflammatory signatures were enriched in the youngest islet autoantibody-positive groups and in patients with the lowest C-peptide values, while higher BMI and type 2 diabetes characteristics were found in older patients. The prognostic relevance was demonstrated by persistent differences in HbA1c at 7 years median follow-up.

Interpretation

This multivariable analysis revealed subgroups of young patients with diabetes that have potential pathogenetic and therapeutic relevance.

Funding

The work was supported by funds from the German Federal Ministry of Education and Research (01KX1818; FKZ 01GI0805; DZD e.V.), the Innovative Medicine Initiative 2 Joint Undertaking INNODIA (grant agreement No. 115797), the German Robert Koch Institute, and the German Diabetes Association.

Integration of Infant Metabolite, Genetic, and Islet Autoimmunity Signatures to Predict Type 1 Diabetes by Age 6 Years

CONTEXT

Biomarkers that can accurately predict risk of type 1 diabetes (T1D) in genetically predisposed children can facilitate interventions to delay or prevent the disease.

OBJECTIVE

This work aimed to determine if a combination of genetic, immunologic, and metabolic features, measured at infancy, can be used to predict the likelihood that a child will develop T1D by age 6 years.

METHODS

Newborns with human leukocyte antigen (HLA) typing were enrolled in the prospective birth cohort of The Environmental Determinants of Diabetes in the Young (TEDDY). TEDDY ascertained children in Finland, Germany, Sweden, and the United States. TEDDY children were either from the general population or from families with T1D with an HLA genotype associated with T1D specific to TEDDY eligibility criteria. From the TEDDY cohort there were 702 children will all data sources measured at ages 3, 6, and 9 months, 11.4% of whom progressed to T1D by age 6 years. The main outcome measure was a diagnosis of T1D as diagnosed by American Diabetes Association criteria.

RESULTS

Machine learning-based feature selection yielded classifiers based on disparate demographic, immunologic, genetic, and metabolite features. The accuracy of the model using all available data evaluated by the area under a receiver operating characteristic curve is 0.84. Reducing to only 3- and 9-month measurements did not reduce the area under the curve significantly. Metabolomics had the largest value when evaluating the accuracy at a low false-positive rate.

CONCLUSION

The metabolite features identified as important for progression to T1D by age 6 years point to altered sugar metabolism in infancy. Integrating this information with classic risk factors improves prediction of the progression to T1D in early childhood.

High-Throughput Multiplex Electrochemiluminescence Assay Applicable to General Population Screening for Type 1 Diabetes and Celiac Disease

Objective: Large-scale screening of the general population for islet autoantibodies (IAbs) to detect type 1 diabetes (T1D) has started worldwide. The standard screening method of separate radio-binding assay (RBA) for each IAb is an inefficient bottleneck. Furthermore, most positive results by RBA in screening of general population individuals without a clinical diagnosis of T1D are low-affinity and not predictive of future diabetes. Research Design and Methods: We have developed and validated a novel 6-Plex assay based on electrochemiluminescence (ECL) technology that combines in a single well high-affinity IAbs (to insulin, GAD, IA-2, and ZnT8), transglutaminase autoantibodies for celiac disease, and severe acute respiratory syndrome coronavirus 2 antibodies. The Autoimmunity Screening for Kids (ASK) provided 880 serum samples, from 828 children aged 1-17 years without diabetes who were previously tested for IAbs using single ECL assays and RBA assays. Results: Levels of all six antibodies in the 6-Plex ECL assay correlated well with respective single ECL assay levels. Similar to single ECL assays, the 6-Plex ECL assay positivity was congruent with the RBA in 95% (35/37) of children who later developed T1D and in 88% (105/119) high-risk children with multiple IAbs. In contrast, only 56% (86/154, P < 0.0001) of children with persistent single IAb by RBA were found to be positive by 6-Plex ECL assay. Of 555 samples negative for all IAbs by RBA, few (0.2%-0.5%) were positive at low levels in the 6-Plex ECL assay. Conclusions: The study demonstrated that the 6-Plex ECL assay compares favorably to the standard RBAs in terms of disease specificity for general population screening in children. The 6-Plex ECL assay was therefore adopted as the primary screening tool in the general population screening ASK program with advantages of high efficiency, low cost, and low serum volume.

Genetic Variants Associated with Neuropeptide Y Autoantibody Levels in Newly Diagnosed Individuals with Type 1 Diabetes

(1) Autoantibodies to the leucine variant of neuropeptide Y (NPY-LA) have been found in individuals with type 1 diabetes (T1D). We investigated the association between the levels of NPY-LA and single nucleotide polymorphisms (SNP) to better understand the genetic regulatory mechanisms of autoimmunity in T1D and the functional impacts of increased NPY-LA levels. (2) NPY-LA measurements from serum and SNP genotyping were done on 560 newly diagnosed individuals with T1D. SNP imputation with the 1000 Genomes reference panel was followed by an association analysis between the SNPs and measured NPY-LA levels. Additionally, functional enrichment and pathway analyses were done. (3) Three loci (DGKH, DCAF5, and LINC02261) were associated with NPY-LA levels (p-value < 1.5 × 10−6), which indicates an association with neurologic and vascular disorders. SNPs associated with variations in expression levels were found in six genes (including DCAF5). The pathway analysis showed that NPY-LA was associated with changes in gene transcription, protein modification, immunological functions, and the MAPK pathway. (4) Conclusively, we found NPY-LA to be significantly associated with three loci (DGKH, DCAF5, and LINC02261), and based on our findings we hypothesize that the presence of NPY-LA is associated with the regulation of the immune system and possibly neurologic and vascular disorders.

Utility of Diabetes Type-Specific Genetic Risk Scores for the Classification of Diabetes Type Among Multiethnic Youth

OBJECTIVE

Genetic risk scores (GRS) aid classification of diabetes type in White European adult populations. We aimed to assess the utility of GRS in the classification of diabetes type among racially/ethnically diverse youth in the U.S.

RESEARCH DESIGN AND METHODS

We generated type 1 diabetes (T1D)- and type 2 diabetes (T2D)-specific GRS in 2,045 individuals from the SEARCH for Diabetes in Youth study. We assessed the distribution of genetic risk stratified by diabetes autoantibody positive or negative (DAA+/-) and insulin sensitivity (IS) or insulin resistance (IR) and self-reported race/ethnicity (White, Black, Hispanic, and other).

RESULTS

T1D and T2D GRS were strong independent predictors of etiologic type. The T1D GRS was highest in the DAA+/IS group and lowest in the DAA-/IR group, with the inverse relationship observed with the T2D GRS. Discrimination was similar across all racial/ethnic groups but showed differences in score distribution. Clustering by combined genetic risk showed DAA+/IR and DAA-/IS individuals had a greater probability of T1D than T2D. In DAA- individuals, genetic probability of T1D identified individuals most likely to progress to absolute insulin deficiency.

CONCLUSIONS

Diabetes type-specific GRS are consistent predictors of diabetes type across racial/ethnic groups in a U.S. youth cohort, but future work needs to account for differences in GRS distribution by ancestry. T1D and T2D GRS may have particular utility for classification of DAA- children.

Costs of Public Health Screening of Children for Presymptomatic Type 1 Diabetes in Bavaria, Germany

OBJECTIVE

We sought to evaluate costs associated with public health screening for presymptomatic type 1 diabetes in 90,632 children as part of the Fr1da study in Bavaria and in forecasts for standard care.

RESEARCH DESIGN AND METHODS

We report on resource use and direct costs for screening-related procedures in the Fr1da study coordination center and laboratory and in participating pediatric practices and local diabetes clinics. Data were obtained from Fr1da study documents, an online survey among pediatricians, and interviews and records of Fr1da staff members. Data were analyzed with tree models that mimic procedures during the screening process. Cost estimates are presented as they were observed in the Fr1da study and as they can be expected in standard care for various scenarios.

RESULTS

The costs per child screened in the Fr1da study were €28.17 (95% CI 19.96; 39.63) and the costs per child diagnosed with presymptomatic type 1 diabetes were €9,117 (6,460; 12,827). Assuming a prevalence of presymptomatic type 1 diabetes of 0.31%, as in the Fr1da study, the estimated costs in standard care in Germany would be €21.73 (16.76; 28.19) per screened child and €7,035 (5,426; 9,124) per diagnosed child. Of the projected screening costs, €12.25 would be the costs in the medical practice, €9.34 for coordination and laboratory, and €0.14 for local diabetes clinics.

CONCLUSIONS

This study provides information for the planning and implementation of screening tests for presymptomatic type 1 diabetes in the general public and for the analysis of the cost-effectiveness of targeted prevention strategies.

Characterising the age-dependent effects of risk factors on type 1 diabetes progression

AIMS/HYPOTHESIS

Age is known to be one of the most important stratifiers of disease progression in type 1 diabetes. However, what drives the difference in rate of progression between adults and children is poorly understood. Evidence suggests that many type 1 diabetes disease predictors do not have the same effect across the age spectrum. Without a comprehensive analysis describing the varying risk profiles of predictors over the age continuum, researchers and clinicians are susceptible to inappropriate assessment of risk when examining populations of differing ages. We aimed to systematically assess and characterise how the effect of key type 1 diabetes risk predictors changes with age.

METHODS

Using longitudinal data from single- and multiple-autoantibody-positive at-risk individuals recruited between the ages of 1 and 45 years in TrialNet's Pathway to Prevention Study, we assessed and visually characterised the age-varying effect of key demographic, immune and metabolic predictors of type 1 diabetes by employing a flexible spline model. Two progression outcomes were defined: participants with single autoantibodies (n=4893) were analysed for progression to multiple autoantibodies or type 1 diabetes, and participants with multiple autoantibodies were analysed (n=3856) for progression to type 1 diabetes.

RESULTS

Several predictors exhibited significant age-varying effects on disease progression. Amongst single-autoantibody participants, HLA-DR3 (p=0.007), GAD65 autoantibody positivity (p=0.008), elevated BMI (p=0.007) and HOMA-IR (p=0.002) showed a significant increase in effect on disease progression with increasing age. Insulin autoantibody positivity had a diminishing effect with older age in single-autoantibody-positive participants (p<0.001). Amongst multiple-autoantibody-positive participants, male sex (p=0.002) was associated with an increase in risk for progression, and HLA DR3/4 (p=0.05) showed a decreased effect on disease progression with older age. In both single- and multiple-autoantibody-positive individuals, significant changes in HR with age were seen for multiple measures of islet function. Risk estimation using prediction risk score Index60 was found to be better at a younger age for both single- and multiple-autoantibody-positive individuals (p=0.007 and p<0.001, respectively). No age-varying effect was seen for prediction risk score DPTRS (p=0.861 and p=0.178, respectively). Multivariable analyses suggested that incorporating the age-varying effect of the individual components of these validated risk scores has the potential to enhance the risk estimate.

CONCLUSIONS/INTERPRETATION

Analysing the age-varying effect of disease predictors improves understanding and prediction of type 1 diabetes disease progression, and should be leveraged to refine prediction models and guide mechanistic studies.

Association of High-Affinity Autoantibodies With Type 1 Diabetes High-Risk HLA Haplotypes

OBJECTIVE

Electrochemiluminescence (ECL) assays are high-affinity autoantibody (Ab) tests that are more specific than Abs detected by traditional radiobinding assays (RBA) for risk screening and prediction of progression to type 1 diabetes. We sought to characterize the association of high-risk human leukocyte antigen (HLA) haplotypes and genotypes with ECL positivity and levels in relatives of individuals with type 1 diabetes.

METHODS

We analyzed 602 participants from the TrialNet Pathway to Prevention Study who were positive for at least 1 RBA diabetes-related Ab [glutamic acid decarboxylase autoantibodies (GADA) or insulin autoantibodies (IAA)] and for whom ECL and HLA data were available. ECL and RBA Ab levels were converted to SD units away from mean (z-scores) for analyses.

RESULTS

Mean age at initial visit was 19.4 ± 13.7 years; 344 (57.1%) were female and 104 (17.3%) carried the high-risk HLA-DR3/4*0302 genotype. At initial visit 424/602 (70.4%) participants were positive for either ECL-GADA or ECL-IAA, and 178/602 (29.6%) were ECL negative. ECL and RBA-GADA positivity were associated with both HLA-DR3 and DR4 haplotypes (all Ps < 0.05), while ECL and RBA-GADA z-score titers were higher in participants with HLA-DR3 haplotypes only (both Ps < 0.001). ECL-IAA (but not RBA-IAA) positivity was associated with the HLA-DR4 haplotype (P < 0.05).

CONCLUSIONS

ECL-GADA positivity is associated with the HLA-DR3 and HLA-DR4 haplotypes and levels are associated with the HLA-DR3 haplotype. ECL-IAA positivity is associated with HLA-DR4 haplotype. These studies further contribute to the understanding of genetic risk and islet autoimmunity endotypes in type 1 diabetes.

Telomere length is not a main factor for the development of islet autoimmunity and type 1 diabetes in the TEDDY study

The Environmental Determinants of Diabetes in the Young (TEDDY) study enrolled 8676 children, 3-4 months of age, born with HLA-susceptibility genotypes for islet autoimmunity (IA) and type 1 diabetes (T1D). Whole-genome sequencing (WGS) was performed in 1119 children in a nested case-control study design. Telomere length was estimated from WGS data using five tools: Computel, Telseq, Telomerecat, qMotif and Motif_counter. The estimated median telomere length was 5.10 kb (IQR 4.52-5.68 kb) using Computel. The age when the blood sample was drawn had a significant negative correlation with telomere length (P = 0.003). European children, particularly those from Finland (P = 0.041) and from Sweden (P = 0.001), had shorter telomeres than children from the U.S.A. Paternal age (P = 0.019) was positively associated with telomere length. First-degree relative status, presence of gestational diabetes in the mother, and maternal age did not have a significant impact on estimated telomere length. HLA-DR4/4 or HLA-DR4/X children had significantly longer telomeres compared to children with HLA-DR3/3 or HLA-DR3/9 haplogenotypes (P = 0.008). Estimated telomere length was not significantly different with respect to any IA (P = 0.377), IAA-first (P = 0.248), GADA-first (P = 0.248) or T1D (P = 0.861). These results suggest that telomere length has no major impact on the risk for IA, the first step to develop T1D. Nevertheless, telomere length was shorter in the T1D high prevalence populations, Finland and Sweden.

Auto-Antikörper-Diagnostik in der Diabetologie – Aktueller Stand der Analytik und klinische Anwendung in Deutschland

Die Messung von spezifischen Autoantikörpern gegen beta-Zellproteine (beta-AAK) hat in den letzten Jahren das diagnostische Repertoire in der Diabetologie erweitert. Das Vorliegen von beta-AAK kann als erstes Stadium in der Entwicklung eines Typ-1-Diabetes mellitus (DM) gewertet werden, ohne dass Symptome bzw. metabolische Veränderungen vorliegen. Da sich diese oft Jahre vor der klinischen Manifestation in Personen mit hohem Erkrankungsrisiko nachweisen lassen, stellen sie wichtige prädiktive und frühdiagnostische Marker dar. Weiterhin kann die Bestimmung von beta-AAK zur Unterscheidung von Patienten mit einem Typ-1-DM auf der einen und Typ-2-DM und Maturity-Onset Diabetes of the Young (MODY) auf der anderen Seite indiziert sein. Auch für die Differenzialdiagnostik von Patienten mit Insulinmangel aufgrund einer autoimmunen Betazelldestruktion und von Patienten mit klinisch sehr ähnlichem „severe-insulin-deficient“-Diabetes, die aber beide eine unterschiedliche Prognose haben, ist die Antikörperdiagnostik zielführend. Die Abschätzung des Risikos für die Entwicklung eines Typ-1-DM bei Patienten, die an autoimmunen Endokrinopathien leiden, stellt einen weiteren Einsatzbereich für beta-AAK dar.

Analytisch sind die beta-AAK mit recht unterschiedlichen Methoden messbar; häufig aber weichen die erhaltenen Messergebnisse bei verschiedenen Testmethoden beträchtlich voneinander ab. Es müssen daher eigene Cut-off Werte vom beauftragten Labor definiert werden, um die erhaltenen Ergebnisse klinisch interpretieren zu können. Zur besseren Vergleichbarkeit der Messergebnisse gibt es derzeit international abgestimmte Harmonisierungsbestrebungen. Für teilnehmende Laboratorien angebotene Ringversuche für die Bestimmungen der Autoantikörper gegen Insulin (IAA), Insulinoma-Antigen 2 (IA-2), Zink Transporter-8 (ZnT8) und Glutamatdecarboxylase (GAD65) können die analytische Qualität ebenfalls verbessern.

Activated but functionally impaired memory Tregs are expanded in slow progressors to type 1 diabetes

AIMS/HYPOTHESIS

Slow progressors to type 1 diabetes are individuals positive for multiple pancreatic islet autoantibodies who have remained diabetes-free for at least 10 years; regulation of the autoimmune response is understudied in this group. Here, we profile CD4⁺ regulatory T cells (Tregs) in a small but well-characterised cohort of extreme slow progressors with a median age 43 (range 31-72 years), followed up for 18-32 years.

METHODS

Peripheral blood samples were obtained from slow progressors (n = 8), age- and sex-matched to healthy donors. One participant in this study was identified with a raised HbA_1c at the time of assessment and subsequently diagnosed with diabetes; this donor was individually evaluated in the analysis of the data. Peripheral blood mononuclear cells (PBMCs) were isolated, and to assess frequency, phenotype and function of Tregs in donors, multi-parameter flow cytometry and T cell suppression assays were performed. Unsupervised clustering analysis, using FlowSOM and CITRUS (cluster identification, characterization, and regression), was used to evaluate Treg phenotypes.

RESULTS

Unsupervised clustering on memory CD4⁺ T cells from slow progressors showed an increased frequency of activated memory CD4⁺ Tregs, associated with increased expression of glucocorticoid-induced TNFR-related protein (GITR), compared with matched healthy donors. One participant with a raised HbA_1c at the time of assessment had a different Treg profile compared with both slow progressors and matched controls. Functional assays demonstrated that Treg-mediated suppression of CD4⁺ effector T cells from slow progressors was significantly impaired, compared with healthy donors. However, effector CD4⁺ T cells from slow progressors were more responsive to Treg suppression compared with healthy donors, demonstrated by increased suppression of CD25 and CD134 expression on effector CD4⁺ T cells.

CONCLUSIONS/INTERPRETATIONS

We conclude that activated memory CD4⁺ Tregs from slow progressors are expanded and enriched for GITR expression, highlighting the need for further study of Treg heterogeneity in individuals at risk of developing type 1 diabetes.

Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in type 1 diabetes

Type 1 diabetes (T1D) is an autoimmune disease in which immune cells destroy insulin-producing beta cells. The aetiology of this complex disease is dependent on the interplay of multiple heterogeneous cell types in the pancreatic environment. Here, we provide a single-cell atlas of pancreatic islets of 24 T1D, autoantibody-positive and nondiabetic organ donors across multiple quantitative modalities including ~80,000 cells using single-cell transcriptomics, ~7,000,000 cells using cytometry by time of flight and ~1,000,000 cells using in situ imaging mass cytometry. We develop an advanced integrative analytical strategy to assess pancreatic islets and identify canonical cell types. We show that a subset of exocrine ductal cells acquires a signature of tolerogenic dendritic cells in an apparent attempt at immune suppression in T1D donors. Our multimodal analyses delineate cell types and processes that may contribute to T1D immunopathogenesis and provide an integrative procedure for exploration and discovery of human pancreatic function.

CGM Metrics Predict Imminent Progression to Type 1 Diabetes: Autoimmunity Screening for Kids (ASK) Study

OBJECTIVE

Children identified with stage 1 type 1 diabetes are at high risk for progressing to stage 3 (clinical) diabetes and require accurate monitoring. Our aim was to establish continuous glucose monitoring (CGM) metrics that could predict imminent progression to diabetes.

RESEARCH DESIGN AND METHODS

In the Autoimmunity Screening for Kids study, 91 children who were persistently islet autoantibody positive (median age 11.5 years; 48% non-Hispanic White; 57% female) with a baseline CGM were followed for development of diabetes for a median of 6 (range 0.2-34) months. Of these, 16 (18%) progressed to clinical diabetes in a median of 4.5 (range 0.4-29) months.

RESULTS

Compared with children who did not progress to clinical diabetes (nonprogressors), those who did (progressors) had significantly higher average sensor glucose levels (119 vs. 105 mg/dL, P < 0.001) and increased glycemic variability (SD 27 vs. 16, coefficient of variation, 21 vs. 15, mean of daily differences 24 vs. 16, and mean amplitude of glycemic excursions 43 vs. 26, all P < 0.001). For progressors, 21% of the time was spent with glucose levels >140 mg/dL (TA140) and 8% of time >160 mg/dL, compared with 3% and 1%, respectively, for nonprogressors. In survival analyses, the risk of progression to diabetes in 1 year was 80% in those with TA140 >10%; in contrast, it was only 5% in the other participants. Performance of prediction by receiver operating curve analyses showed area under the curve of ≥0.89 for both individual and combined CGM metric models.

CONCLUSIONS

TA140 >10% is associated with a high risk of progression to clinical diabetes within the next year in autoantibody-positive children. CGM should be included in the ongoing monitoring of high-risk children and could be used as potential entry criterion for prevention trials.

Persistence of islet autoantibodies after diagnosis in type 1 diabetes

The presence of islet autoantibodies remains a reliable biomarker to identify individuals at high risk of developing type 1 diabetes. As such, these autoantibodies play a pivotal role in understanding the prodrome of diabetes and selecting individuals for both prevention and intervention clinical trials. Over the last few decades, studies have sought to investigate autoantibody prevalence after diabetes onset to better understand ongoing islet autoimmunity; however, many findings are contradictory, and little is known about factors that may influence autoantibody persistence. Generally, glutamate decarboxylase autoantibodies (GADAs) are the most prevalent autoantibodies after diagnosis, particularly in adults, whilst zinc transporter 8 autoantibodies (ZnT8A) prevalence declines more rapidly. However, when studies with islet autoantibody data at diagnosis are considered, it becomes clear that overall islet antigen-2 autoantibodies (IA-2A) tend to persist for longer than GADA or ZnT8A. In this review, we assess the major studies that have contributed to our understanding of autoantibody persistence after diabetes onset and what factors affect this. Islet autoantibodies may provide biomarkers for long-term β-cell function and insights into how to prevent ongoing islet autoimmunity but larger studies collecting samples at and decades after diabetes onset are required to leverage the information they could provide.

High-affinity ZnT8 Autoantibodies by Electrochemiluminescence Assay Improve Risk Prediction for Type 1 Diabetes

CONTEXT

Single ZnT8 autoantibody (ZnT8A) positivity by standard radiobinding assay (RBA) is commonly seen in nondiabetes population-based screening and the risk of progression to type 1 diabetes (T1D) in subjects with single ZnT8A is unknown.

OBJECTIVE

Identify the risk of progression to T1D in individuals positive only for ZnT8A.

METHODS

We developed an electrochemiluminescence (ECL) assay to detect high-affinity ZnT8A and validated it in 3 populations: 302 patients newly diagnosed with T1D, 135 nondiabetic children positive for ZnT8A by RBA among 23 400 children screened by the Autoimmunity Screening for Kids (ASK) study, and 123 nondiabetic children multiple autoantibody positive or single ZnT8A positive by RBA participating in the Diabetes Autoimmunity Study in the Young (DAISY).

RESULTS

In 302 patients with T1D at diagnosis, the positivity for ZnT8A was 62% both in RBA and ECL. Among ASK 135 participants positive for RBA-ZnT8A, 64 were detected ZnT8A as the only islet autoantibody. Of these 64, only 9 were confirmed by ECL-ZnT8A, found to be of high affinity with increased T1D risk. The overall positive predictive value of ECL-ZnT8A for T1D risk was 87.1%, significantly higher than that of RBA-ZnT8A (53.5%, P < .001). In DAISY, 11 of 2547 children who had no positivity previously detected for other islet autoantibodies were identified as single ZnT8A by RBA; of these, 3 were confirmed positive by ECL-ZnT8A and all 3 progressed to clinical T1D.

CONCLUSION

A large proportion of ZnT8A by RBA are single ZnT8A with low T1D risk, whereas ZnT8A by ECL was of high affinity and high prediction for T1D development.

Establishing reference intervals for islet autoantibodies in Han Chinese type 1 diabetes

Currently, islet autoantibodies (IAbs) constitute the most reliable marker for detecting the autoimmune process of type 1 diabetes (T1D). However, there are no appropriate reference intervals (RIs) to interpret the results of IAbs in China. In this study, we aimed to establish the RIs of four common IAbs based on the Han Chinese population and evaluate their clinical diagnostic values in patients with T1D. We collected 177 blood samples from healthy volunteers to detect the levels of IAbs directed against insulin (IAA), glutamic acid decarboxylase-65 (GADA), insulinoma antigen 2 (IA-2A), and zinc transporter-8 (ZnT8A) using a chemiluminescence immunoassay. RIs were calculated using nonparametric 95th percentile intervals in accordance with the Clinical and Laboratory Standards Institute guidelines, and their clinical diagnostic values were evaluated by detecting the levels of IAbs of 140 blood samples from patients with T1D in a clinical setting. We defined 138 individuals as the apparently healthy population from the 177 healthy volunteers based on the exclusion criteria. No association between the levels of the four IAbs and gender (p > .05) and age (p > .05) were found in the apparently healthy population. The combined RIs for GADA, IA-2A, ZnT8A, and IAA were 0-1.78 IU/mL, 0-3.91 IU/mL, 0-2.36 AU/mL, and 0-0.58 COI, respectively. Overall, the diagnostic efficiency for the four IAbs, especially for GADA and IAA, were improved by using the RIs established in this study. The RIs for IAbs established in this study will be a valuable tool for disease diagnosis and the therapeutic management of T1D in a clinical setting.